Details
| Original language | English |
|---|---|
| Title of host publication | WWW´22 |
| Subtitle of host publication | Companion Proceedings of the Web Conference 2022 |
| Pages | 344-348 |
| Number of pages | 5 |
| ISBN (electronic) | 9781450391306 |
| Publication status | Published - 16 Aug 2022 |
| Event | 31st ACM Web Conference, WWW 2022 - Virtual, Online, France Duration: 25 Apr 2022 → 29 Apr 2022 |
Abstract
Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia [21]. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users' featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.
Keywords
- collaborative learning, collaborative search, e-learning, personalised knowledge graphs
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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WWW´22 : Companion Proceedings of the Web Conference 2022. 2022. p. 344-348.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Personal Knowledge Graphs
T2 - 31st ACM Web Conference, WWW 2022
AU - Ilkou, Eleni
N1 - Funding Information: The author would like to thank Prof. Dr. Maria-Esther Vidal for the fruitful discussion, guidance, and insightful comments. This work is funded by EU H2020 project KnowGraphs (GA no. 860801).
PY - 2022/8/16
Y1 - 2022/8/16
N2 - Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia [21]. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users' featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.
AB - Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia [21]. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users' featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.
KW - collaborative learning
KW - collaborative search
KW - e-learning
KW - personalised knowledge graphs
UR - http://www.scopus.com/inward/record.url?scp=85137444488&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2203.08507
DO - 10.48550/arXiv.2203.08507
M3 - Conference contribution
AN - SCOPUS:85137444488
SP - 344
EP - 348
BT - WWW´22
Y2 - 25 April 2022 through 29 April 2022
ER -